Automatic grader for programming assignment using source code analyzer

Yulianto, Susilo Veri; Liem, Inggriani

doi:10.1109/icodse.2014.7062687

Cited by 15 publications

(8 citation statements)

References 1 publication

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“…The student may then self-check the correctness of her solution: if the solution fails for some input argument (i.e., the code aborts or produces a wrong result), the student is correspondingly informed from the RISCAL execution output and may correct her solution. Since solutions have been self-checked, it can be expected that mostly correct solutions will be submitted; furthermore, the results can be by this logic-based execution framework automatically graded with respect to its formal contract (extending the functionality of conventional autograder software [25] which automatically grades programs by testing [9] or static code analysis [26]). While we are not responsible for any course that is suitable to embody the vision sketched above, we will approach some lecturers responsible for the beginners' education in algorithm and software development at JKU; we hope to convince them by a demonstration of the capabilities of RISCAL (with appropriately prepared examples and lecturing materials) to experimentally include some of these envisioned elements into their own courses.…”

Section: Courses On Modeling and Programmingmentioning

confidence: 99%

Theorem and Algorithm Checking for Courses on Logic and Formal Methods

Schreiner

2019

Electron. Proc. Theor. Comput. Sci.

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The RISC Algorithm Language (RISCAL) is a language for the formal modeling of theories and algorithms. A RISCAL specification describes an infinite class of models each of which has finite size; this allows to fully automatically check in such a model the validity of all theorems and the correctness of all algorithms. RISCAL thus enables us to quickly verify/falsify the specific truth of propositions in sample instances of a model class before attempting to prove their general truth in the whole class: the first can be achieved in a fully automatic way while the second typically requires our assistance. RISCAL has been mainly developed for educational purposes. To this end this paper reports on some new enhancements of the tool: the automatic generation of checkable verification conditions from algorithms, the visualization of the execution of procedures and the evaluation of formulas illustrating the computation of their results, and the generation of Web-based student exercises and assignments from RISCAL specifications. Furthermore, we report on our first experience with RISCAL in the teaching of courses on logic and formal methods and on further plans to use this tool to enhance formal education.

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Section: Courses On Modeling and Programmingmentioning

confidence: 99%

Theorem and Algorithm Checking for Courses on Logic and Formal Methods

Schreiner

2019

Electron. Proc. Theor. Comput. Sci.

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“…In [40], the authors are assessing several code quality related aspects of JavaScript programs such as the style, programming errors and complexity with metrics using industrial tools. In [45], the authors also rely on industrial tools to analyse the style of C, Java and PHP programs. In [46], C++ programs are assessed with respect to a set of coding rules to encourage learners to produce high quality codes.…”

Section: Code Qualitymentioning

confidence: 99%

Automated Code Assessment for Education: Review, Classification and Perspectives on Techniques and Tools

Combéfis

2022

Software

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Automatically assessing code for learning purposes is a challenging goal to achieve. On-site courses and online ones developed for distance learning both require automated ways to grade learners’ programs to be able to scale and manage a large public with a limited teaching staff. This paper reviews recent automated code assessment systems. It proposes a systematic review of the possible analyses they can perform with the associated techniques, the kinds of produced feedback and the ways they are integrated in the learning process. It then discusses the key challenges for the development of new automated code assessment systems and the interaction with human grading. In conclusion, the paper draws several recommendations for new research directions and for possible improvements for automatic code assessment.

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“…Pembelajaran tidak hanya bisa dilakukan pada komputer desktop saja, namun dapat juga dilakukan menggunakan perangkat bergerak (mobile) (Han and Shin, 2016). Pembelajaran pemrograman juga dapat dilakukan menggunakan sistem E-Learning (Danutama and Liem, 2013;Yulianto and Liem, 2014).…”

Section: Pendahuluanunclassified

Penggalian Perilaku Pemain dalam Penentuan Tipe Permainan pada E-Learning Pemrograman Berbasis Gamification

Priyambadha

Pradana

Bachtiar

2020

JTIIK

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Salah satu kompetensi utama yang harus dimiliki oleh lulusan dari jurusan atau program studi bidang keilmuan komputer adalah kemampuan programming (membuat program). Berbagi informasi untuk meningkatkan kualitas pembelajaran pemrograman telah dilakukan di banyak kampus di Indonesia. Salah satunya adalah penerapan media pembelajaran online atau disebut juga sebagai E-Learning. Salah satu sistem pembelajaran yang paling umum, yang didukung oleh teknologi informasi, adalah e-learning. Namun, banyak juga sistem e-learning tidak mencapai tujuan yang diinginkan karena ketidakpatuhan dan kurangnya pengetahuan tentang teknik dan metode untuk pengembangan sistem informasi online. Tujuan yang dicapai antara lain efisiensi, efektifitas, motivasi dan keterlibatan siswa. Pendekatan gamification dapat digunakan untuk meningkatkan beberapa hal tersebut demi tercapainya tujuan pembelajaran online. Dalam sistem Code Maniac, terdapat beberapa elemen gamification yang digunakan, yaitu poin pengalaman, poin aktivitas dan badge. Namun, hal tersebut terbukti masih kurang meningkatkan motivasi mahasiswa dalam menggunakan Code Maniac. Pendekatan player-centric memungkinkan sistem menyesuaikan gameplay yang sesuai dengan gaya bermain seorang pemain. Untuk dapat mewujudkan sebuah media pembelajaran yang berorientasi pada pemain, maka dibutuhkan sebuah mekanisme untuk mengenali karakteristik pemainnya. Pada penelitian ini menekanan metode yang digunakan untuk menggali perilaku permainan. Penggalian pola dilakukan pada data log proses per pemain dan per sesi yang berjumlah 136 proses. Kemudian proses tersebut dikelompokan sesuai dengan kedekatan atau kesamaan karakteristik bermain. Hasil penentuan jumlah kelompok yang paling optimal adalah k=2 dan k=3. Untuk itu pengelompokan dilakukan dan menghasilkan 2 kelompok dan 3 kelompok data. Kelompok tersebut dapat menjadi dasar untuk untuk penentuan gameplay pada Code Maniac. AbstractOne of the main competencies that must be possessed by graduates of departments or study programs in computer science is programming skills (making programs). Sharing information to improve the quality of programming learning has been done on many campuses in Indonesia. One of them is the application of online learning media or also called the E-Learning.. One of the most common learning systems, supported by information technology, is e-learning. However, many e-learning systems do not achieve the desired goals because of non-compliance and lack of knowledge about techniques and methods for developing information systems online. The objectives achieved include efficiency, effectiveness, motivation and student involvement. The gamification approach can be used to improve some of these things in order to achieve online learning goals. In the Code Maniac system, there are several gamification elements used, namely experience points, activity points and badges. However, this proved to be still lacking in increasing student motivation in using Code Maniac. The player-centric approach allows the system to adjust the gameplay to suit a player's playing style. To be able to realize a player-oriented learning media, a mechanism is needed to recognize the characteristics of the players. In this study emphasizes the methods used to explore game behavior. Pattern mining is performed on process log data per player and per session which amounts to 136 processes. Then the process is grouped according to the closeness or similarity of playing characteristics. The results of determining the most optimal number of groups are k = 2 and k = 3. For this reason, grouping is done and produces 2 groups and 3 groups of data. The group can be the basis for determining the gameplay in Code Maniac.

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Automatic grader for programming assignment using source code analyzer

Cited by 15 publications

References 1 publication

Theorem and Algorithm Checking for Courses on Logic and Formal Methods

Theorem and Algorithm Checking for Courses on Logic and Formal Methods

Automated Code Assessment for Education: Review, Classification and Perspectives on Techniques and Tools

Penggalian Perilaku Pemain dalam Penentuan Tipe Permainan pada E-Learning Pemrograman Berbasis Gamification

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